The Superior Electrical Conductivity and Anodic Stability of Vanadium-doped Ti<sub>4</sub>O<sub>7</sub>
Bibliographic record
Abstract
Magnéli phase titanium oxides (MPTOs, Ti n O 2 n -1 , 4 ≤ n 10) 1 are promising alternatives to carbon-based materials in aqueous electrochemical technologies, 2–5 but their effectiveness is limited by their instability at strongly oxidizing potentials. Ti 4 O 7 and Ti 5 O 9 , the most electrically-conductive phases, passivate under the harsh conditions demanded by fuel cells, 6 batteries, 7 and electrochemical devices for water treatment applications. 8 Among other properties relevant to electrodes, doping of MPTOs with vanadium, iron, and chromium improves the oxidation stability as demonstrated by higher onset temperatures in the thermograms of doped Ti 4 O 7 heated in air. 9 To investigate the influence of doping on their anodic stability, electrodes of Ti 4 O 7 doped with V, Cr, and Fe were investigated by electrochemical accelerated life testing (ALT). V- and Fe-doping are shown to significantly improve the electrical conductivity compared to pristine Ti 4 O 7 . In the case of V-doping, the anodic stability was significantly improved as measured by the time-to-failure (TTF) during electrochemical ALT. References S. Andersson, B. Collén, U. Kulenstierna, and M. Magnéli, Acta. Chem. Scand ., 11 , 1641 (1957). J. R. Smith, F. C. Walsh, and R. L. Clarke, J. Appl. Electrochem ., 28 , 1021 (1998). F. C. Walsh and R. G. A. Wills, Electrochim. Acta , 55, 6342 (2010). B. Xu, H. Y. Sohn, Y. Mohassab, and Y. Lan, RSC Adv ., 6 , 79706 (2016). B. P. Chaplin, Acc. Chem. Res ., 52, 596 (2019). W. H. Kao, P. Patel, and S. L. Haberichter, J. Electrochem. Soc. , 144 , 1907 (1997). G. Chen, C. C. Waraksa, H. Cho, D. D. Macdonald, and T. E. Mallouka, J. Electrochem. Soc. , 150 , E423 (2003). D. Bejan, J. D. Malcolm, L. Morrison, and N. J. Bunce, Electrochim. Acta , 54 , 5548 (2009). J. T. English and D. P. Wilkinson, ECS J. Solid State Sci. Technol. , 10 , 034004 (2021).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".